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Remaining useful life (RUL) prediction plays an important role in improving the availability and productivity of systems. To improve the accuracy of real-time RUL prediction during system operation, we propose a modeling method for real-time RUL prediction based on adaptive kernel window width density. First, a non-parametric kernel density estimation (KDE) real-time RUL prediction model is proposed, and a window width model with adaptive kernel window width density is established by introducing a local density factor in the window width selection. The local density of sample points is calculated by the k-nearest neighbor distance, and the KDE is performed by adaptively selecting the window width value according to the local density of sample points in the region of non-uniform distribution of sample points. As the monitoring data changes in real time, the kernel density estimates of known samples are used to recursively update the kernel density estimates of new samples. Moreover, the logarithmic transformation of random variables and space mapping are used in the establishment of the RUL prediction model. A model of logarithmic kernel diffeomorphism transformation is established to solve the boundary shift problem of kernel estimation in the prediction to improve the prediction accuracy. Finally, the validity of the method is verified through case studies, and the accuracy of the model is judged using evaluation quasi-measures.

A modified multi-sensor information fusion method for hydraulic fault diagnosing system is proposed in this paper. Combined with the improved JDL data fusion model and the hierarchical processing idea, it can solve some difficult fault diagnosis problems of hydraulic system. The adaptive weighted least squares estimation method is used to clean the data and extract the feature in data layer. The multi-parallel particle swarm optimisation (PSO)-Hopfield neural network is applied in feature level for local diagnosis. When the time-airspace integrates, there is a direct data communication and feedback between each level based on modified Dempster-Shafer (D-S) evidence theory in decision-making level. The final diagnosis has a direct data communication and feedback between each level, and it can make the information of each level based on data mining as soon as possible. Experimental results show that the method in conflicted evidence has high correct rate and can avoid index explosion and fix the fault exactly.

Hidden Markov models (HMMs) are well known for their application in temporal pattern recognition. However, they have such problems that easy to converge to local optimal solutions and not fit well with sparsely connected time-series data. It is therefore imperative to have good methods to explore a more suitable choice, which can avoid the problems mentioned above as much as possible. We proposed a recurrent hidden Markov models (RHMM) and particle swarm optimisation (PSO) approach. Under this framework, convergence of local optimal solutions and fit with sparsely connected time-series data are solved by adjusting disturbed extremum. Furthermore, PSO is used to train the RHMM and determine the model parameters. Our approach has been applied to cursive handwriting recognition. The results demonstrate that the RHMM/PSO can provide meaningful insights for pattern recognition like fast lexicon-free recognition. It makes lexicon-free result alternatives extraction efficiently, word hypotheses evaluation quickl...